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An R and S Plus Companion to Multivariate Analysis

software packages, S-Plus or R. This book sets out how to use these packages for these analyses in a concise and easy-to-use way, and will save;

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An R Companion for Applied Statistics II

in an intermediate or multivariate statistics course and provides one or two ways to run each analysis in R. The book has been designed to be an;

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An Introduction to Applied Multivariate Analysis with R

apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as;

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Multivariate Data Analysis

graduate and postgraduate students undertaking statistical analysis for business degrees, providing an application-oriented introduction to;

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Multivariate Analysis for the Biobehavioral and Social Sciences

An insightful guide to understanding and visualizing multivariate statistics using SAS(R), STATA(R), and SPSS(R) Multivariate Analysis for;

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Multivariate Time Series Analysis

. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and;

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Multivariate Survival Analysis and Competing Risks

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It;

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Multivariate Survival Analysis and Competing Risks

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It;

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Smoothing of Multivariate Data

of Multivariate Data provides an illustrative and hands-on approach to the multivariate aspects of density estimation, emphasizing the use;

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Methods of Multivariate Statistics

availability of computer packages such as S-plus and SAS, multivariate methods once too complex to tackle are now within reach of most researchers and;

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Applied Multivariate Analysis

half of the text moves from defining the basics to explaining models. Topics include regression and the analysis of variance; principal;

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Exploratory Multivariate Analysis by Example Using R

"An introduction to exploratory techniques for multivariate data analysis, this book covers the key methodology, including principal;

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Univariate Bivariate & Multivariate Stat

, behavioral, and natural sciences, Univariate, Bivariate, and Multivariate Statistics Using R offers an easy-to-use guide for performing data analysis;

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Essentials of Multivariate Data Analysis

. Accessible to students and researchers without a substantial background in statistics or mathematics, Essentials of Multivariate Data Analysis;

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Robust Multivariate Analysis

by theory. The robust techniques are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor;

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Exploratory Multivariate Analysis by Example Using R

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four;

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Exploratory Multivariate Analysis by Example Using R

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four;

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Multivariate Statistical Methods

methods in an intelligent manner. With updated information on multivariate analyses, new references, and R code included, this book continues to;

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Multivariate Statistical Methods

methods in an intelligent manner. With updated information on multivariate analyses, new references, and R code included, this book continues to;

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Effect Sizes For Research

. Tips on the use of SPSS, SAS, R, and S-Plus are provided. The book's broad disciplinary appeal results from its inclusion of a variety;

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Effect Sizes for Research

. Tips on the use of SPSS, SAS, R, and S-Plus are provided. The book's broad disciplinary appeal results from its inclusion of a variety;

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Computational Statistics

and comparison of distributions, and multivariate analysis. It uses a range of examples to demonstrate how R can be employed to tackle;

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Discovering Knowledge In Data

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever;

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Applied Univariate, Bivariate, & Multivariate Stat istics

and R contains an accessible introduction to statistical modeling techniques commonly used in the social and natural sciences. The text offers;

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The Basics of S-PLUS

introduction to R, as well as S-PLUS New exercises have been added; Includes a comparison of S-PLUS and R Well-suited for self-study;

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An R and S-Plus Companion to Applied Regression

analysis, an introduction to applying GLMs, a discussion of model diagnostics and useful instructions on writing customized functions' - Jeff Gill;

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Multivariate Time Series Analysis and Applications

Written specifically for time series courses Multivariate Time Series Analysis and its Applications is designed for an advanced time series;

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